This Python script uses Monte Carlo simulations to estimate the future price of a given stock (in this example, AAPL). It utilizes historical stock price data obtained from Yahoo! Finance.
Before running this code, you'll need to ensure you have the necessary Python packages installed. These include:
- yfinance
- seaborn
- numpy
- pandas
- matplotlib
You can install these packages using pip:
pip3 install yfinance seaborn numpy pandas matplotlib-
Clone or download the repository to your local machine.
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Make sure you have the required packages installed (see Prerequisites).
To run the simulation, execute the Python script MonteCarlo.py. The script will download historical stock price data for the specified asset from Yahoo! Finance and perform the Monte Carlo simulation.
The core function Monte_Carlo takes several parameters:
period(int): Number of trading days for the simulation.n_scenarios(int): Number of simulation scenarios.mu(float): Mean daily return.sigma(float): Daily volatility.last_price(float): Last known price.
It returns a DataFrame containing simulated stock prices.
# Simulate stock prices
sim = Monte_Carlo_example(period=252, n_scenarios=10000, mu=mean, sigma=vol, last_price=last_price)
# Plot the distribution of the estimated price
plt.figure(figsize=(12,8))
ax1 = sns.histplot(data=sim.iloc[-1, :], bins=30, kde=True, color='skyblue', stat='density')
ax1.set(xlabel='Price', ylabel='Density', title=f'Distribution of {asset} Estimated Price in 252 Days')
plt.show()
# Plot the paths of the simulations
plt.figure(figsize=(12,8))
ax2 = sim.plot(legend=False, title=f'Simulated Paths of {asset}')
ax2.set(xlabel='Days', ylabel='Price')
plt.show()This will generate a distribution plot of the estimated price and a plot of the simulated paths for the specified asset.
This project is licensed under the MIT License.